Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
The currente date is
## [1] "2020-04-24"
First check the 20 states with the largest number of deaths.
## date state fips cases deaths
## 2908 2020-04-24 New York 36 271621 16162
## 2906 2020-04-24 New Jersey 34 102196 5617
## 2898 2020-04-24 Michigan 26 36627 3084
## 2897 2020-04-24 Massachusetts 25 50969 2556
## 2889 2020-04-24 Illinois 17 39658 1804
## 2915 2020-04-24 Pennsylvania 42 40298 1786
## 2881 2020-04-24 Connecticut 9 23921 1764
## 2879 2020-04-24 California 6 41368 1619
## 2894 2020-04-24 Louisiana 22 26140 1601
## 2884 2020-04-24 Florida 12 30525 1045
## 2885 2020-04-24 Georgia 13 21575 889
## 2890 2020-04-24 Indiana 18 13680 741
## 2926 2020-04-24 Washington 53 13120 731
## 2896 2020-04-24 Maryland 24 16618 723
## 2912 2020-04-24 Ohio 39 15169 690
## 2880 2020-04-24 Colorado 8 12255 672
## 2921 2020-04-24 Texas 48 23650 625
## 2925 2020-04-24 Virginia 51 11596 413
## 2909 2020-04-24 North Carolina 37 8052 270
## 2877 2020-04-24 Arizona 4 6045 268
For these 20 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
First check the 20 counties with the largest number of deaths.
## date county state fips cases deaths
## 85808 2020-04-24 New York City New York NA 150484 11157
## 85807 2020-04-24 Nassau New York 36059 32765 1867
## 85359 2020-04-24 Wayne Michigan 26163 15407 1443
## 84712 2020-04-24 Cook Illinois 17031 27616 1220
## 85827 2020-04-24 Suffolk New York 36103 30606 1035
## 85835 2020-04-24 Westchester New York 36119 26632 989
## 85736 2020-04-24 Essex New Jersey 34013 12110 975
## 85731 2020-04-24 Bergen New Jersey 34003 14363 934
## 84329 2020-04-24 Los Angeles California 6037 18545 850
## 84422 2020-04-24 Fairfield Connecticut 9001 10227 662
## 85738 2020-04-24 Hudson New Jersey 34017 13011 640
## 85274 2020-04-24 Middlesex Massachusetts 25017 11681 585
## 85340 2020-04-24 Oakland Michigan 26125 6804 585
## 85749 2020-04-24 Union New Jersey 34039 11208 542
## 84423 2020-04-24 Hartford Connecticut 9003 4570 511
## 85327 2020-04-24 Macomb Michigan 26099 5022 504
## 86203 2020-04-24 Philadelphia Pennsylvania 42101 11877 449
## 85741 2020-04-24 Middlesex New Jersey 34023 9789 413
## 84426 2020-04-24 New Haven Connecticut 9009 6286 396
## 86795 2020-04-24 King Washington 53033 5691 393
For these 20 counties, I check the number of new cases and the number of new deaths.
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they are more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
Since the daily postive rate can fluctuate a lot, here I only illustrae the cumulative positave rate across time, for four states with grade A data. Of course since this is an R markdown file, you can modify the source code and check for other states.
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 pillar_1.4.3 compiler_3.6.2 tools_3.6.2
## [5] digest_0.6.23 evaluate_0.14 lifecycle_0.1.0 tibble_2.1.3
## [9] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.4 yaml_2.2.1
## [13] xfun_0.12 gridExtra_2.3 withr_2.1.2 dplyr_0.8.4
## [17] stringr_1.4.0 knitr_1.28 grid_3.6.2 tidyselect_1.0.0
## [21] cowplot_1.0.0 glue_1.3.1 R6_2.4.1 rmarkdown_2.1
## [25] purrr_0.3.3 farver_2.0.3 scales_1.1.0 htmltools_0.4.0
## [29] assertthat_0.2.1 colorspace_1.4-1 ggsignif_0.6.0 labeling_0.3
## [33] stringi_1.4.5 lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4